期刊
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
卷 23, 期 11, 页码 2410-2418出版社
IEEE COMPUTER SOC
DOI: 10.1109/TVCG.2017.2734599
关键词
Tracking; Deep Learning; Augmented Reality
资金
- FRQ-NT New Researcher Grant [2016NC189939]
- NSERC Discovery Grant [RGPIN-2014-05314]
- REPARTI Strategic Network
- Nvidia
We present a temporal 6-DOF tracking method which leverages deep learning to achieve state-of-the-art performance on challenging datasets of real world capture. Our method is both more accurate and more robust to occlusions than the existing best performing approaches while maintaining real-time performance. To assess its efficacy, we evaluate our approach on several challenging RGBD sequences of real objects in a variety of conditions. Notably, we systematically evaluate robustness to occlusions through a series of sequences where the object to be tracked is increasingly occluded. Finally, our approach is purely data-driven and does not require any hand-designed features: robust tracking is automatically learned from data.
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